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13,064
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13,064 results for “predictions”
High-dimensional deconstruction of pancreatic cancer identifies tumor microenvironmental and developmental stemness features that predict survival
<p>Annotated single cell count data for "High-dimensional deconstruction of pancreatic cancer identifies tumor microenvironmental and developmental stemness features that predict survival"</p>
Dataset associated with the study titled "Systematic discovery of regulatory motifs associated with human insulator sites" It includes data used for predicting insulator-associated DNA-binding proteins.
<p><strong>This dataset was used in the study on the prediction of insulator-associated DNA-binding proteins.</strong></p> <p>The three files included here are intended for use with the code available in the following GitHub repository:<br><a href="https://github.com/reposit2/insulator" target="_new" rel="noopener">https://github.com/reposit2/insulator</a></p> <p>To use the data, extract all three files and place them in the same directory as the code files.</p>
Streamflow Predictions using Machine Learning with Data Reformation
<p>Reference: Tran, Vinh Ngoc, Valeriy Y. Ivanov, and Jongho Kim. "Data reformation–A novel data processing technique enhancing machine learning applicability for predicting streamflow extremes." <em>Advances in Water Resources</em> 182 (2023): 104569. https://doi.org/10.1016/j.advwatres.2023.104569</p>
Mesh data for multi-impeller mixing performance prediction in stirred tanks using mean age theory approach
<p>The upload files include mesh data for all configurations studied in the research: multi-impeller mixing performance prediction in stirred tanks using mean age theory approach.</p>
A3D database: structure-based predictions of protein aggregation for the human proteome
<p>A3D database: structure-based predictions of protein aggregation for the human proteome</p>
Experimental House of Building Energetics Model Predictive Control experiment data
<p>This repository hosts data from the Experimental House of Building Energetics (EHBE) of Hydro-Québec in Shawinigan.</p>
Datasets for ``Magnetohydrodynamics predicts heavy-tailed distributions of axion-photon conversion''
<p>This directory contains an index.html file with links to the run<br> directories and idl plotting routines with secondary data for the other<br> figures for the paper "Magnetohydrodynamics predicts heavy-tailed<br> distributions of axion-photon conversion" by P. Carenza, R. Sharma,<br> M. C. D. Marsh, A. Brandenburg, & E. Mueller. If anything turns out to<br> be incomplete, please email brandenb@nordita.org.</p> <p>We provide details of the simulations of small-scale dynamo used for<br> the paper and provide also 3D snapshots of the magnetic fields for the<br> kinetic stage (bb8.dat) and the saturated stage (bb10.dat).</p> <p>Here, we also provide data files containing the probability of conversion<br> of photon to axion for MHD realization of the magnetic field. These<br> probabilities have been calculated using Eqs. (4) and (6) of our paper<br> and the data files bb8.dat and bb10.dat provided in the previous upload.<br> In data files relevant for Figs. 2, 5, and 6, the first and second<br> columns are the x and y coordinates, the third and fourth column<br> gives the probability of conversion for the linearly polarised light<br> in the y and x direction, respectively, and the fifth column gives the<br> probability for the unpolarised light. Additionally, we have data files,<br> "power_mag_bb8.dat" and "power_mag_bb10.dat," which contain data for<br> the magnetic field power spectra for the kinetic and saturated cases.</p> <p>Content:<br> ALP_MHD.zip (115.4 MB)<br> zip file with all run directories.</p> <p>bb10.dat (1.6 GB)<br> Run S, magnetic field as a single array, bb(512,512,512,3),</p> <p>bb10gau.dat (1.6 GB)<br> Gaussian magnetic field for Run S (same spectrum as bb10.dat)</p> <p>bb8.dat (1.6 GB)<br> Run K, magnetic field as a single array, bb(512,512,512,3),</p> <p>bb8gau.dat (1.6 GB)<br> Gaussian magnetic field for Run K (same spectrum as bb8.dat)</p> <p>D512_Pm20a2_bb8_versus_z.mpg (4.2 MB)<br> Anination of different z cross-sections for Run K</p> <p>D512_Pm20a2_versus_t.mpg (50.3 MB)<br> Anination of time evolution for Run S</p> <p>D512_Pm20a2_versus_z.mpg (5.8 MB)<br> Anination of different z cross-sections for Run S</p> <p>D512_Pm20a_gaussian9_versus_z.mpg (14.3 MB)<br> Anination of different z cross-sections for Gaussian version of Run K</p> <p>D512_Pm20a_gaussian_bb10_versus_z.mpg (9.5 MB)<br> Anination of different z cross-sections for Gaussian version of Run S</p> <p>D512_Pm20a_versus_t.mpg (19.3 MB)<br> Anination of time evolution for Run K</p> <p>rr10.dat (1.1 GB)<br> Density for Run S, rr(512,512,512).</p> <p>rr8.dat (1.1 GB)<br> Density for Run K, rr(512,512,512).</p> <p><br> Analysis files for figures (34.5 MB each)</p> <p>fig2a_bb8.dat<br> fig2b_bb8.dat<br> fig2c_bb8.dat</p> <p>fig2d_bb10.dat<br> fig2e_bb10.dat<br> fig2f_bb10.dat</p> <p>fig5a_bb8.dat<br> fig5b_bb10.dat</p> <p>fig6a_mask.dat<br> fig6b_clip.dat</p> <p>power_mag_bb8.dat (6 kB)<br> power_mag_bb10.dat (6 kB)</p>
Empirical Exploration of Open-Source Issues for Predicting Privacy Compliance
<p>The dataset for the above publication RQ1</p>
Benchmarking splice variant prediction algorithms using massively parallel splicing assays
<p>Dataset, jupyter notebooks, and support python modules for "Benchmarking splice variant prediction algorithms using massively parallel splicing assays" (Smith and Kitzman, 2023)</p>
Video of MVP Final version - OD and occupancy prediction for Bilbobus
<p>The video shows the final version of the solutions Transport Analytics and Occupancy API, deployed by Citio for the Bilbobus transport network, in the context of the REACH project. </p>
Dataset for manuscript "Turnover number predictions for kinetically uncharacterized enzymes using machine and deep learning"
<p>Dataset for the github repository containing the code for the manuscript "Turnover number predictions for kinetically uncharacterized enzymes using machine and deep learning".</p>
Monte Carlo Arithmetic Instrumented DeepGOPlus Protein Function Predictions
<p>This dataset contains the perturbed protein function predictions by the DeepGOPlus model excluding the Diamond tool component. The model was perturbed with Verrou, an implementation of Monte Carlo Arithmetic (MCA), a stochastic arithmetic technique that injects noise into a program that simulates changes in a user's execution environment. The folders contain pkl files that can be read with the Pandas python library to load up dataframes containing the predictions and original values. Each file is one MCA sample run across the entire DeepGOPlus test set.</p> <p>The folder "Verrou_All" contains predictions where the entirety of the model was instrumented with MCA.</p> <p>The folder "Verrou_TF" contains predictions where only the Tensorflow library was instrumented with MCA.</p> <p>The folder "Fuzzy_Python" contains predictions where only the Python interpreter was instrumented with MCA.</p> <p>The folder "VPREC_Outbound_Mode" contains predictions where the virtual precision of the floating point operations was reduced witht the VPREC precision simulator tool in outbound mode.</p> <p>The folder "VPREC_Inbound_Mode" contains predictions where the virtual precision of the floating point operations was reduced witht the VPREC precision simulator tool in inbound mode.</p> <p>More information can be found by consulting <a href="https://arxiv.org/abs/2212.06361">this paper</a> or <a href="https://github.com/big-data-lab-team/deepgoplus-stability">this Github repository</a>.</p>
Learning to Predict and Improve Build Success Rates in Package Ecosystems
<p>We present a method to predict software build success. We leverage the heavily parameterized package recipes from Spack to produce a training data set on builds, and we use Graph Neural Networks to learn whether a given package configuration will build successfully or not. We apply our tool to the U.S. Exascale Project’s software stack, and we demonstrate its effectiveness in predicting whether a given package will build successfully. Our method can be used to predict outcomes of software builds with high accuracy, eliminating the need for manual builds. Furthermore, we demonstrate how these build predictions can be used to improve the rate of build success in the Spack package manager.</p>
Prediction-Powered Inference: Data Sets
<p>Data sets used in the paper "Prediction-Powered Inference."</p>
Precipitation predictability drives evolution of drought strategies in the common poppy, Papaver rhoeas
<ol> <li>Current climate change leads to an increase in the frequency and intensity of droughts and to decreased precipitation predictability. The few studies investigating plant evolutionary responses to contrasting predictability regimes showed that intrinsic precipitation predictability shapes plant phenotypic variation, drives evolution of phenotypic plasticity, and can vary in strength and direction of selection. This suggests that the selection pressure induced by decreased precipitation predictability may lead to plants coping better with severe drought events. </li> <li>To investigate whether past precipitation predictability influences plant responses to different drought conditions, we performed a common-garden experiment applying control, short-term and long-term drought treatments on seedlings of <em>Papaver rhoeas</em> (Papaveraceae) whose progenitors experienced less versus more precipitation predictability treatments for three consecutive generations. In addition, to assess whether competition modulates plant responses, half of the plants were grown together with the widespread herbaceous plant <em>Galium album </em>(Rubiaceae).</li> <li>In the presence of long drought episodes, plants whose progenitors experienced less predictable precipitation survived longer than those whose progenitors experienced more predictable precipitation. Furthermore, plants whose progenitors experienced less predictable precipitation had lower biomass, which is likely to reduce water loss via transpiration, and, across all drought treatments, they showed lower root investment. However, no trait differences were detected under competition, indicating that interspecific competition may limit the expression of evolutionary responses to changes induced by precipitation predictability.</li> <li>Altogether our results indicate that lower precipitation predictability mainly promotes the evolution of drought-escape and drought-avoidance strategies. Overall, our experiment highlights that precipitation predictability is an important evolutionary driver of plant functional responses, potentially shifting evolutionary trajectories of plants under increasing intensity of drought events. </li> </ol>
10,000 stochastic prediction RMMs generated by SMP of MJO-Net
<p>The numpy array has shape of (710, 10000, 70, 6). It contains information of 710 MJO events. Each MJO event has 70-length 10,000 stochastic MJO RMMs predictions.</p> <p>Assume this numpy array named MJO_Data.</p> <p>To extract the 1,000 46-day MJO stochastic predictoins for 710 MJO events, use:</p> <p>MJO_Pred = MJO_Data[:, :, 10:, 2:4]</p> <p>To extract the paired 46-day ECMWF real-time predictoin for 710 MJO events, use:</p> <p>MJO_ECMWF = MJO_Data[:, :1, 10:, 4:6]</p> <p>To extract the paired 46-day MJO RMMs observation, use:</p> <p>MJO_OB = MJO_Data[:, :1, 10:, :2]</p>
Blood cell differential count discretization modeling predicts survival in adults reporting to the emergency room: a retrospective cohort study
<p><strong>Objectives</strong>: to assess survival predictivity of baseline blood cell differential count (BCDC), discretized according to two different methods, in adults visiting the Emergency Room (ER) for illness or trauma over one-year. </p> <p><strong>Design</strong>: Retrospective cohort study of hospital records. </p> <p><strong>Setting</strong>: Tertiary care public hospital in northern Italy. </p> <p><strong>Participants</strong>: 11052 patients aged > 18 years, consecutively admitted to the ER in one year, and for whom BCDC collection was indicated by ER medical staff at first presentation.</p> <p><strong>Primary outcome</strong>: Survival was the referral outcome for explorative model development. Automated BCDC analysis at baseline assessed hemoglobin, red cell mean volume (MCV) and distribution-width (RDW), platelet distribution-width (PDW), plateletcrit (PCT), absolute red blood cells, white blood cells, neutrophils, lymphocytes, monocytes, eosinophils, basophils, and platelets. Discretization cutoffs were defined by Benchmark and Tailored methods. Benchmark cutoffs were stated on laboratory reference values (CLSI). Tailored cutoffs for linear, sigmoid-shaped and for U-shaped distributed variables were discretized by Maximally Selected Rank Statistics and by Optimal-Equal Hazard Ratio respectively. Explanatory variables (age, gender, ER admission during SARS-CoV2 surges, in-hospital admission) were analyzed using Cox multivariable regression. ROC curves were drawn by sum of Cox-significant variables for each method.</p> <p><strong>Results</strong>: Of 11052 patients (median age 67 years, IQR 51–81, 48% female), 59% (n=6489) were discharged and 41% (n=4563) were admitted in hospital. After a 306-day median follow up (IQR 208–417 days), 9455 (86%) patients were alive and 1597 (14%) deceased. Increased HRs were associated with age >73-years (HR=4.6 CI=4.0–5.2), in-hospital admission (HR=2.2 CI=1.9–2.4), ER admission during SARS-CoV2 surges (Wave-I HR=1.7 CI=1.5–1.9); Wave-II HR=1.2 CI=1.0–1.3). Gender, hemoglobin, MCV, RDW, PDW, neutrophils, lymphocytes and eosinophils counts were significant in overall. Benchmark-BCDC model included basophils and platelet count (AUROC 0.74). Tailored-BCDC model included monocyte counts and plateletcrit (AUROC 0.79).</p> <p><strong>Conclusions</strong>: baseline discretized BCDC provides meaningful insight regarding Emergency Room patients survival.</p>
Nicotiana model for augustus gene prediction
<p>Nicotiana model for augustus gene prediction.</p>
Underlying dataset for the manuscript titled "Identification of RTS,S/AS01 vaccine-induced humoral biomarkers predictive of protection against controlled human malaria infection"
<p><span>The antibody binding data included in this dataset is associated with the manuscript “Spreng, R.L., Seaton, K.E. et al., <em><span>Identification of RTS,S/AS01 vaccine-induced humoral biomarkers predictive of protection against controlled human malaria infection; </span></em><span>Accepted for Publication in JCI Insight (2024)</span>. These data come from the characterization of serum samples from four controlled human malaria infection (CHMI) clinical trials of the RTS,S vaccine, including NCT01366534 (referred to as MAL068), NCT01857869 (referred to as MAL071), NCT03162614 (referred to as MAL092), and NCT03824236 (referred to as MAL102).</span></p>
Maternal anxiety and prenatal support predict pregnancy length and infant characteristics at birth during the pandemic
<p><strong>ABSTRACT</strong></p> <p>According to the theoretical prediction of the smoke detector principle, heightened anxiety as a personality trait could lead to better survival and reproductive success in unpredictable environments. Yet published studies suggest that maternal anxiety negatively affects gestation and birth outcomes, which may lead to decreased reproductive success. To resolve this conundrum, we studied the association between maternal anxiety and birth outcomes during the COVID-19 pandemic. Data collected from 1080 expectant mothers from the Corona Mums Project included maternal trait anxiety (STAI X2), perceived emotional support (PES), duration of gestation, and infant parameters at birth taken from hospital health records. After adjusting for covariates, multiple regression models showed that STAI X2 positively predicted infant birth weight (β=0.07, p=0.023) and length (β=0.08, p=0.010). PES positively predicted only birth weight (β=0.06, p=0.048). Neither of these factors was significantly related to duration of gestation, although the association with PES was of borderline significance (β=0.06, p=0.053). The results suggest that maternal increased ability to detect threats, indicated by her trait anxiety during the unpredictable conditions of COVID-19, was associated with better infant characteristics at birth. This may improve infant survival and maternal reproductive success, implying that increased trait anxiety could be adaptive during environmental threats.</p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.